Emotive advisory system and method

ABSTRACT

Information about a device may be emotively conveyed to a user of the device. Input indicative of an operating state of the device may be received. The input may be transformed into data representing a simulated emotional state. Data representing an avatar that expresses the simulated emotional state may be generated and displayed. A query from the user regarding the simulated emotional state expressed by the avatar may be received. The query may be responded to.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of application Ser. No. 12/110,712,filed Apr. 28, 2008, which claims the benefit of U.S. ProvisionalApplication No. 60/914,152, filed Apr. 26, 2007, the disclosures ofwhich are hereby incorporated in their entirety by reference herein.

BACKGROUND

U.S. Pub. No. 2007/0074114 to Adjali et al. discloses a human-computerinterface for automatic persuasive dialog between the interface and auser and a method of operating such an interface. The method includespresenting a user with an avatar or animated image for conveyinginformation to the user and receiving real time data relating to apersonal attribute of the user, so as to modify the visual appearanceand/or audio output of the avatar or animated image as a function of thereceived data relating to a personal attribute of the user.

SUMMARY

A method for emotively conveying information to a user of a device mayinclude receiving input indicative of an operating state of the device,transforming the input into data representing a simulated emotionalstate and generating data representing an avatar that expresses thesimulated emotional state. The method may also include displaying theavatar, receiving a query from the user regarding the simulatedemotional state expressed by the avatar and responding to the query.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an embodiment of an emotive advisory systemfor an automotive vehicle.

FIG. 2 is a block diagram of a portion of the emotive advisory system ofFIG. 1.

FIG. 3 is a block diagram of another portion of the emotive advisorysystem of FIG. 1.

FIG. 4 is a block diagram of a portion of the automotive vehicle of FIG.1.

FIG. 5 is a block diagram of another portion of the automotive vehicleof FIG. 1.

FIG. 6 is a block diagram of yet another portion of the emotive advisorysystem of FIG. 1.

FIG. 7 is a block diagram of an embodiment of a communications managerfor the emotive advisory system of FIG. 1.

FIG. 8 is a block diagram of another embodiment of a communicationsmanager for the emotive advisory system of FIG. 1.

FIGS. 9A and 9B are block diagrams of portions of an emotional enginefor the emotive advisory system of FIG. 1.

FIG. 10 is a block diagram of a spoken dialog manager for the emotiveadvisory system of FIG. 1.

FIG. 11 is another block diagram of the spoken dialog manager of FIG.10.

FIG. 12 is a flow chart depicting an algorithm employed by the spokendialog manager of FIG. 10.

FIG. 13 is a block diagram of an emotional speech synthesizer for theemotive advisory system of FIG. 1.

FIGS. 14A and 14B are flow charts depicting algorithms employed by theemotional speech synthesizer of FIG. 13.

FIG. 15 is a block diagram of a display rendering engine andtext-to-speech engine for the emotive advisory system of FIG. 1.

FIG. 16 is a block diagram of a learning module for the emotive advisorysystem of FIG. 1.

FIGS. 17A through 17C are flow charts depicting algorithms employed bythe learning module of FIG. 16.

FIG. 18 is a block diagram of a task manager for the emotive advisorysystem of FIG. 1.

FIG. 19 is another block diagram of the task manager of FIG. 18.

FIGS. 20A and 20B are flow charts depicting algorithms employed by thetask manager of FIG. 18.

FIG. 21 is a block diagram of an agent configured to interact with theemotive advisory system of FIG. 1.

DETAILED DESCRIPTION

Referring now to FIG. 1, an embodiment of an emotive advisory system(EAS) 10, inter alia, assists an occupant/user 12 of a vehicle 14 inoperating the vehicle 14 and in accessing information sources 16 n,e.g., web servers, etc., remote from the vehicle 14 via a network 17. Ofcourse, other embodiments of the EAS 10 may be implemented within thecontext of any type of device and/or machine. For example, the EAS 10may accompany a household appliance, hand held computing device, etc.Certain embodiments of the EAS 10 may be implemented as an integratedmodule that may be docked with another device and/or machine. A user maythus carry their EAS 10 with them and use it to interface with devicesand/or machines they wish to interact with. Other configurations andarrangements are also possible.

In the embodiment of FIG. 1, sensors 18 detect inputs generated by theoccupant 12 and convert them into digital information for a computer 20.The computer 20 receives these inputs as well as inputs from theinformation sources 16 n and vehicle systems 22. The computer 20processes these inputs and generates outputs for at least one of theoccupant 12, information sources 16 n and vehicle systems 22.Actuators/outputs, etc. 24 convert the outputs for the occupant 12 froma digital format into a format that may be perceived by the occupant 12,whether visual, audible, tactile, haptic, etc.

The occupant 12 may, in some embodiments, communicate with the EAS 10through spoken dialog that follows rules of discourse. For example, theoccupant 12 may ask “Are there any good restaurants in the area?” Inresponse, the EAS 10 may query appropriate information sources 16 n and,together with geographic location information from the vehicle systems22, determine a list of highly rated restaurants near the currentlocation of the vehicle 14. The EAS 10 may answer with the simulateddialog: “There are a few. Would you like to hear the list?” Anaffirmative response from the occupant 12 may cause the EAS 10 to readthe list.

The occupant 14 may also command the EAS 10 to alter certain parametersassociated with the vehicle systems 22. For example, the occupant 14 maystate “I feel like driving fast today.” In response, the EAS 10 may ask“Would you like the drivetrain optimized for performance driving?” Anaffirmative response from the occupant 12 may cause the EAS 10 to alterengine tuning parameters for enhanced performance.

In some embodiments, the spoken dialog with the EAS 10 may be initiatedwithout pressing any buttons or otherwise physically providing input tothe EAS 10. This open microphone functionality allows the occupant 12 toinitiate a conversation with the EAS 10 in the same way the occupant 12would initiate a conversation with another occupant of the vehicle 14.

The occupant 12 may also “barge in” on the EAS 10 while it is speaking.For example, while the EAS 10 is reading the list of restaurantsmentioned above, the occupant 12 may interject “Tell me more aboutrestaurant X.” In response, the EAS 10 may cease reading the list andquery appropriate information sources 16 n to gather additionalinformation regarding restaurant X. The EAS 10 may then read theadditional information to the occupant 12.

In some embodiments, the actuators/outputs 24 include a screen thatselectively displays an avatar. The avatar may be a graphicalrepresentation of human, animal, machine, plant, vehicle, etc. and mayinclude features, e.g., a face, etc., that are capable of visuallyconveying emotion. The avatar may be hidden from view if, for example, aspeed of the vehicle 14 is greater than a threshold which may bemanufacturer or user defined. The avatar's voice, however, may continueto be heard. Of course, any suitable type of display technology, such asa holographic or head-up display, may be used.

The avatar's simulated human emotional state may depend on a variety ofdifferent criteria including an estimated emotional state of theoccupant 12, a condition of the vehicle 14 and/or a quality with whichthe EAS 10 is performing a task, etc. For example, the sensors 18 maydetect head movements, speech prosody, biometric information, etc. ofthe occupant 12 that, when processed by the computer 20, indicate thatthe occupant 12 is angry. In one example response, the EAS 10 may limitor discontinue dialog that it initiates with the occupant 12 while theoccupant 12 is angry. In another example response, the avatar may berendered in blue color tones with a concerned facial expression and askin a calm voice “Is something bothering you?” If the occupant 12responds by saying “Because of this traffic, I think I'm going to belate for work,” the avatar may ask “Would you like me to find a fasterroute?” or “Is there someone you would like me to call?” If the occupant12 responds by saying “No. This is the only way . . . ,” the avatar mayask “Would you like to hear some classical music?” The occupant 12 mayanswer “No. But could you tell me about the upcoming elections?” Inresponse, the EAS 10 may query the appropriate information sources 16 nto gather the current news regarding the elections. During the query, ifthe communication link with the information sources 16 n is strong, theavatar may appear happy. If, however, the communication link with theinformation sources 16 n is weak, the avatar may appear sad, promptingthe occupant to ask “Are you having difficulty getting news on theelections?” The avatar may answer “Yes, I'm having trouble establishinga remote communication link.”

During the above exchange, the avatar may appear to become frustratedif, for example, the vehicle 14 experiences frequent acceleration anddeceleration or otherwise harsh handling. This change in simulatedemotion may prompt the occupant 14 to ask “What's wrong?” The avatar mayanswer “Your driving is hurting my fuel efficiency. You might want tocut down on the frequent acceleration and deceleration.” The avatar mayalso appear to become confused if, for example, the avatar does notunderstand a command or query from the occupant 14. This type of dialogmay continue with the avatar dynamically altering its simulatedemotional state via its appearance, expression, tone of voice, wordchoice, etc. to convey information to the occupant 12.

The EAS 10 may also learn to anticipate requests, commands and/orpreferences of the occupant 12 based on a history of interaction betweenthe occupant 12 and the EAS 10. For example, the EAS 10 may learn thatthe occupant 12 prefers a cabin temperature of 72. Fahrenheit whenambient temperatures exceed 80. Fahrenheit and a cabin temperature of78. Fahrenheit when ambient temperatures are less than 40. Fahrenheitand it is a cloudy day. A record of such climate control settings andambient temperatures may inform the EAS 10 as to this apparentpreference of the occupant 12. Similarly, the EAS 10 may learn that theoccupant 12 prefers to listen to local traffic reports upon vehiclestart-up. A record of several requests for traffic news followingvehicle start-up may prompt the EAS 10 to gather such information uponvehicle start-up and ask the occupant 12 whether they would like to hearthe local traffic. Other learned behaviors are also possible.

These learned requests, commands and/or preferences may be supplementedand/or initialized with occupant-defined criteria. For example, theoccupant 12 may inform the EAS 10 that it does not like to discusssports but does like to discuss music, etc. In this example, the EAS 10may refrain from initiating conversations with the occupant 12 regardingsports but periodically talk with the occupant 12 about music.

Referring now to FIG. 2, the computer 20 communicates, bi-directionally,with (i) a wireless network interface 26 to reach the informationsources 16 n illustrated in FIG. 1 and (ii) a hub, e.g., a USB hub 28,to reach peripheral devices such as buttons 30, video camera 32, vehicleBUS controller 34, sound device 36 and a private vehicle network 38. Thecomputer 20 also communicates with a display 40 on which, as explainedabove, an avatar may be rendered. Other configurations and arrangementsare, of course, also possible.

Referring now to FIG. 3, the wireless network interface 26 may establisha communication link with the remote web server 16 a via, for example,an Evolution-Data Optimized (EVDO) device 42 and the network 17, e.g.,cellular broadband/Internet/etc. EVDO devices provide link-level, e.g.,IEEE 802.1, packet data services over a cellular network. Informationfrom the wireless network interface 26 is provided to the EVDO 42 andtransmitted via Internet Protocol (IP) to a network (not shown) linkedto the network 17. Transmission Control Protocol (TCP) and UniversalDatagram Protocol (UDP) data packets are transported by the IP packets.Sockets are used to provide a connection-oriented (TCP) orconnection-less (UDP) connection to the web server 16 a. In otherembodiments, any suitable wireless communication technique, such asOrthogonal Frequency Domain Multiplexed (OFDM), Metropolitan AreaNetwork (MAN), WiMax, etc., may be used.

Referring now to FIG. 4, the vehicle bus controller 34 may provide aport for the computer 20 illustrated in FIG. 2 to exchange informationregarding the vehicle systems 22. The vehicle bus controller 34 of FIG.4 exchanges information signals with, for example, a powertrain controlmodule 46 and instrument cluster 48 via a Data Link Connector (DLC) 50.Similarly, the vehicle bus controller 34 may exchange informationsignals regarding a navigation system 52, HVAC system 54, etc. via asmart junction box 56 and the DLC 50. Such communication within avehicle may be conducted via a Controller Area Network (CAN) bus, aLocal Area Network bus or a Resistor Ladder Network (RLN) (also referredto as cascaded resistors). Any suitable communication technique,however, may be used.

Referring now to FIG. 5, the sound device 36 may receive analog audioinputs from a microphone 58. The sound device 36 converts the analoginputs to digital outputs for the computer 20 illustrated in FIG. 2. Thesound device 36 may also receive digital inputs from the computer 20representing, for example, the voice of the avatar. The sound device 36converts the digital inputs to analog outputs for an amplifier 60. Theseamplified analog outputs may be played on a collection of speakers 62.

Referring now to FIG. 6, the computer 20 may include device drivers 64corresponding to certain hardware, such as the hardware illustrated inFIG. 2. The device drivers 64 interact with software modules 66 and assuch, may provide an interface between the hardware illustrated in FIG.2 and the software modules 66. The software modules 66 provide and/orreceive outputs and/or inputs that, as discussed below, are used by avariety of subsystems within the EAS 10 illustrated in FIG. 1.

A peripheral interface bus, such as a USB hub driver 68, CAN bus,BLUETOOTH, etc., de-multiplexes/multiplexes information from/to the USBhub 28 illustrated in FIG. 2 for a video camera driver 70, microphonedriver 72, buttons driver 74, vehicle bus controller driver 76, privatevehicle network driver 78, speakers driver 80 and display driver 81. Ofcourse, other and/or different drivers may be included as desired.

Several broad categories of networks may be used including ring, mesh,star, fully connected, line, tree and bus. USB is a hybrid star/treenetwork that operates at 1.5, 12, 480 and 4800 Mbit/second. The networkmay be wired or wireless, and commercial products are widely availablethat use industry standard chipsets such as the USB251x family of chipsfrom SMSC Semiconductor. A USB implementation may have adequatethroughput to support 6 audio channels, a video channel and variousother devices. The network may be either wired or wireless. Otherconfigurations are, of course, also possible.

The video camera driver 70 provides digital video data via an I/O streamto an image recognition module 82. The image recognition module 82 ofFIG. 6 processes the digital video data and outputs parametersindicative of visual cues from the occupant 12 illustrated in FIG. 1.Parameters, of course, are mathematical abstractions of occupantfeatures that may be tracked using suitable image recognition technique.These parameters may include occupant recognition, gaze direction, headnods, smile index, lip size and shape, pupil position, pupil size,nostril location, eyebrows, face profile, etc. Other parameters, such asscalp line and wrinkles, etc., may also be used.

Occupant recognition is a parameter that characterizes the identity ofthe occupant 12. The gaze direction, head nods, smile index and lipmovement are parameters that characterize movements of the occupant 12.

In the embodiment of FIG. 6, the image recognition module 82 recognizesoccupant features, tracks them from image to image and recognizespatterns of movement. These movements are then classified intoparticular gestures via a gesture classification algorithm, e.g.,sequential vector machine, neural network, etc., resulting in theparameters indicative of visual cues from the occupant 12. In otherembodiments, the image recognition module 82 may employ any suitableimage recognition technique. Several such algorithms/methods are knownin the art. One approach uses spectral graph techniques to cluster shapeand appearance features, then groups the clusters into time-varyingfacial gestures. A second approach uses a classifier based onreal-valued hyperplanes implemented on specialized hardware for rapidprocessing. A third method combines an Adaptive View-based AppearanceModel (AVAM) with a 3D view registration algorithm. Other methods arealso possible.

The microphone driver 72 provides audio via an I/O stream to anautomatic speech recognition/voice recognition module 84. The automaticspeech recognition/voice recognition module 84 of FIG. 6 processesdigital audio data and outputs parameters indicative of audio cues fromthe occupant 12 illustrated in FIG. 1. In other embodiments, theautomatic speech recognition/voice recognition module 84 may alsoprocess the one or more parameters, such as lip movement, output by theimage recognition module 82. The parameters output by the automaticspeech recognition/voice recognition module 84 include an N-Best listand occupant recognition. Any suitable parameters, however, may be used.

An N-Best list, in this example, may comprise an utterance recording,i.e., a sound recording, of a portion of speech from the occupant 12illustrated in FIG. 1 and a set of associated recognition entries. Eachrecognition entry may include a textual version of the utterancerecording along with a confidence parameter. The confidence parameterindicates the degree of confidence with which the text accuratelycaptures the words associated with the utterance recording. Eachrecognition entry may also include a natural language version of theutterance recording. For example, the spoken sentence “The brown dog ranfast.” may be represented in natural language as(((The(brown*(dog)))(fast*(ran))).

In the embodiment of FIG. 6, once audio data is received, phonologicalfeatures are extracted from the digital data and sentence endpoints areidentified using, for example, end pointing algorithms. Phonologicalfeatures for each sentence are compared with a list of possiblesentences as a hypothesis set. The hypotheses the system determines havethe highest confidence of being the correct transcription of thephonological data are selected and placed in the N-best list along witha confidence level. An utterance recording for a particular sentence isrecorded into an audio file which, as discussed below, may later beanalyzed for emotional content. The automatic speech recognition/voicerecognition module 84 also outputs a natural language version of thephonological features which contains syntactic information.

As discussed above, a particular utterance recording may result inseveral recognition entries. For example, if the occupant 12 illustratedin FIG. 1 says “get me the news,” the automatic speech recognition/voicerecognition module 84 may produce two hypothesis based only on the soundsignal it receives: “get me the news” and “get me the reviews.” Thediscourse context associated with dialog between the occupant 12 and theEAS 10 illustrated in FIG. 1 may be used to select between multiplehypotheses.

Discourse contextual analysis algorithms may be implemented by using theknowledge of the current topic to determine the appropriateness of aparticular hypothesis. For example, a discussion regarding restaurantsis not likely to involve a query about the news. As a result, a currentdiscourse context may be used to reprioritize the N-best list,introducing context into the recognition confidence.

Within a particular context, such as “news” or “restaurants,” a certainsub-set of recognizable sentences is more likely to occur than others.For example, the sentence “give me the news” may be more likely to occurin the “news” context while the sentence “give me the reviews” may bemore likely to occur in the “restaurants” context. If an N-best listcontains sentences from different contexts, the sentence from thecurrent context may be assigned a higher recognition confidence,potentially reordering the N-best list.

The context may also be used to determine whether a particular utteranceis addressed to the EAS 10 illustrated in FIG. 1 or whether it isaddressed to other occupants (not shown) of the vehicle 14. For example,if the EAS 10 announces that fuel is low and requests driver input as tohow to proceed, if the driver responds with “I am hungry,” the EAS 10may use context to help determine if the phrase “I am hungry” wasaddressed to the EAS 10. With the vehicle systems interface describedabove, it may first find if there are multiple occupants in the vehicle14 by getting input from an Occupant Classification System (OCS). Ifthere are multiple occupants, the EAS 10 may then assume that the out ofcontext phrase “I am hungry” was part of a discussion with the othervehicle occupants rather than a request to find a restaurant directed tothe EAS 10.

The identity of the speaker may be determined by combining voicerecognition from an automatic speech recognition/voice recognitionmodule discussed below, image recognition, e.g., lip movements, etc.,and acoustics to determine the speaker's location, etc.

The occupant recognition parameter indicates whether the automaticspeech recognition/voice recognition module 84 recognizes the voice ofthe occupant 12 illustrated in FIG. 1 and also to whom the voicebelongs. In the embodiment of FIG. 6, the occupant recognition parameteris generated by comparing a sound recording captured from the occupant12 with stored sound features associated with a known list of occupants.Any suitable voice recognition algorithm, however, may be used.

The buttons driver 74 provides digital information indicative ofwhether, for example, the buttons 30 illustrated in FIG. 2 are beingpressed via an Application Programming Interface (API) to a buttoninterface module 86. The button interface module 86 of FIG. 6 processesthis information and outputs a parameter indicative of such buttonpresses. The embodiment described here is multimodal in that pressing abutton is equivalent to speaking a command, and alters the context ofthe discourse. Therefore, button pushes may be used to determine theoccupant's location and identity, and may alter the selection ofrecognition hypothesis in the N-Best list.

The vehicle bus controller driver 76 provides, for example, CAN messagesincluding a CAN I.D., a message type and 8 data bytes via an API to aCAN bus interface 88. Of course, any suitable vehicle network, e.g.,flex ray, J-1850, etc., and associated protocol may be used. The CAN businterface 88 processes these CAN messages and outputs protocolindicative of a state of a vehicle system, e.g., throttle position,wheel speed, fuel level, fuel consumption, transmission gear, braketorque, etc.

The CAN bus interface 88 also receives inputs from, as discussed below,agents in the form of EAS protocol. Any suitable protocol, however, maybe used. The CAN bus interface 88 repackages these messages into CANprotocol and forwards them to the USB hub 28 illustrated in FIG. 2 viathe drivers 68,76. In some embodiments, these messages may includecommands and/or operating parameters for the vehicle systems 22illustrated in FIG. 4. For example, a message may include information asto how the powertrain control module 46 illustrated in FIG. 4 shouldcontrol the engine (not shown). Other arrangements are also possible.

The private vehicle network driver 78 provides digital informationassociated with certain vehicle systems not in communication with theCAN illustrated in FIG. 4 to an auxiliary network interface 90 via anAPI. For example, window position and window motor voltage informationmay not be broadcast via the CAN but may be accessible through theprivate vehicle network driver 78. Additionally, devices installed onthe private vehicle network may have analog/digital and digital/analogconverters that allow the EAS 10 illustrated in FIG. 1 to ascertain thestatus of conventional controls connected to an RLN and also to takecontrol of an RLN network to emulate the use of conventional controls.

The auxiliary network interface 90 of FIG. 6 obtains analog signals andconverts them into a digital protocol. The auxiliary network interface90 then converts the digital protocol into EAS protocol for use bycertain EAS agents discussed in further detail below. Such informationmay be indicative of a state of the vehicle systems not in communicationwith the CAN illustrated in FIG. 4.

Similar to the CAN bus interface 88, the auxiliary network interface 90also receives inputs from certain EAS agents in the form of EASprotocol. The auxiliary network interface 90 repackages these messagesinto a format for a digital to analog conversion. Analog outputs maythen be delivered to, for example, the actuators 24 illustrated in FIG.1 and/or various RLNs (not shown) within the vehicle 14 illustrated inFIG. 1.

An avatar controller 92, in the embodiment of FIG. 6, may be a computerprogram and rendering engine that supports rendering of the avatar onthe display 40 illustrated in FIG. 2 using one of several sets ofApplication Programming Interfaces (API). Many rendering engines may beused for this purpose, including Renderware, Torque Game Engine, TV3D,3D Game Studio, C4 Engine, DX Studio, Crystal Space, Game Blender, etc.These use several graphics oriented APIs including Direct3D, OpenGL,DirectX, SDL, OpenAL, etc.

The avatar controller 92 receives numerical as well as textual inputs tocontrol geometric transformations of the avatar and its synthesizedtextual outputs. In the embodiment of FIG. 6, these inputs includeparameters indicative of button rendering, avatar emotion,text-to-speech control and emotively tagged text. Other and/or differentinputs may also be used.

The button rendering parameter informs the avatar controller 92 as tohow to render any virtual buttons visible from the display 40illustrated in FIG. 2. The avatar gestures and text-to-speech controlinform the avatar controller 92 as to how to render movement and facialexpressions of the avatar as the avatar speaks. For example, the avatargestures may control hand movements, gaze direction, etc., of theavatar. The text-to-speech control may control when to begin, end,suspend, abort or resume any text-to-speech operations. The avataremotion and emotively tagged text, as discussed in detail below, informthe avatar controller 92 as to how to render movement and facialexpressions of the avatar as the avatar expresses emotion.

Briefly, avatar emotion in the embodiment of FIG. 6 includes weightedvector representations of a set of emotions for the avatar. Emotivelytagged text includes marked-up phrases that indicate emotional contentassociated with certain words of the phrase. The avatar appearance isdynamically altered to express emotion, indicate speech is taking placeand/or convey information, etc. The avatar expression is controlled bymanipulating specific points on the surface of the avatar. In a computergenerated avatar, a mathematical representation of a 3D surface of aphysical avatar is made, typically using polygonal modelingtechniques/algorithms. Alternatively, the surface may be modeled usingspline curves (such as NURBS), subdivision surfaces, equation basedrepresentations, etc. In polygonal modeling the approach is toapproximate the real surface with many conforming flat polygons. Eachpolygon may be associated with color(s) and a texture map that definessuch optical characteristics as surface roughness, color variation,reflectivity, specularity, etc. The model may then be illuminated usinga shading algorithm that assumes a distribution of point light sourcesand ambient light. Shading methods generally trade off rendering speedagainst how natural the image looks, and several methods are known inthe art such as ray tracing, Nebulaud shading, Gouraud Shading, Phongshading, Cel-shading, etc. In some embodiments, the naturalness of theshading should match the naturalness of the voice and the phraseology ofthe avatar.

The appearance of the avatar may be dynamically manipulated by movingthe position of the polygon vertices, changing the color and texture ofpolygons, changing the color and position of the lights, etc. in therendering engine.

The avatar controller 92 processes the above described inputs andprovides image frames, via a stream, to the display driver 81. Thedisplay driver 81 processes the image frames using any suitabletechnique and outputs them to the display 40 illustrated in FIG. 2.

The avatar controller 92 also provides digital audio data associatedwith the above inputs to the speakers driver 80 via an I/O stream. Thespeakers driver 80 provides this data to the USB hub driver 68 fordelivery to the sound device 36 illustrated in FIG. 5.

The avatar controller 92 generates several outputs that, as explainedbelow, may be used as timing information to facilitate control of theavatar. In the embodiment of FIG. 6, the avatar controller 92 outputsparameters indicative of a completed text string, sentence, word,syllable, viseme, gesture, etc. by the avatar (collectively referred toherein as avatar events.) Of course, other and/or different parametersmay be used. Whether the avatar has completed a textual string and thecurrent lip position of the avatar may be used to determine whetherand/or when to interrupt the avatar's current speech with, for example,speech of a more urgent nature.

The lip movements of the avatar may be animated using a set ofpredefined lip positions that are correlated to each allophone ofspeech. A number corresponding to a viseme may be used to index eachposition which is either morphed or concatenated to the rest of theavatar's face. There are standard viseme sets such as the Disney visemesand several others that are in common use. The text-to-speech engineproduces a stream of visemes that are time synchronized to the speechthat is produced. The visemes are streamed to the rendering engine toaffect lip movement.

In the embodiment of FIG. 6, an HTTP client 94 may establish aninter-process socket connection with one of the remote servers 16 nillustrated in FIG. 1. The HTTP client 94 forms an HTTP URL, sends itthrough the socket connection to the remote server 16 n and waits for aresponse. The remote server 16 n formats a response, for example, in XMLand sends the response through the socket connection to the HTTP client94. The HTTP client 94 may then reformat the response into, for example,EAS protocol for use by an EAS agent.

As described herein, the EAS 10 illustrated in FIG. 1 and other relatedsystems include a number of hardware and/or software modules thatcommunicate with each other through, for example, inter-processcommunication. For clarity, techniques that may be used to facilitatesuch communication are described with reference to FIGS. 7 and 8 ratherthan addressing such communication issues in detail when discussingother Figures provided herein. Other suitable communicationarchitectures, however, may also be used.

Referring now to FIG. 7, an embodiment of a communications manager 96,such as a message oriented middleware solution (MOM), e.g., SunJava,Java message service, advanced message queuing protocol, etc., includesa set of databases 98 and a set of semaphores 99 that permit a hardwareinterface program 100 to broadcast/receive information to/from modulesof the EAS 10 illustrated herein. Transactions to/from the databases 98may be atomic and may be synchronized using the semaphores 99.

In certain embodiments, some of the software modules 66 described inFIG. 6 and elsewhere herein may each implement the communications modelof FIG. 7. For example, the hardware interface program 100 may representthe image recognition module 82, the button interface 86, etc.,illustrated in FIG. 6.

In the embodiment of FIG. 7, software modules 102 are logically groupedinto several categories: input functions 104, input/output functions106, output functions 108 and blocked output functions 110. As apparentto those of ordinary skill, the software modules 102 implement thetransactions to/from the databases 98 using any suitable I/O functions,and convert any hardware protocol, e.g., CAN messages, etc., into, forexample, EAS protocol, XML, etc.

Data from the hardware interface program 100 to be processed by any ofthe software modules 102 is stored in one or more of the databases 98.Output data from the hardware interface program 100 for the input/outputfunctions 106, output functions 108 and blocked output functions 110 isstored in an outputs database 112 and accessed by these functions asnecessary. Output data from the hardware interface program 100 for theblocked output functions 110 is stored in the semaphores database 99and, similar to above, accessed by the blocked output functions 110 asnecessary.

Data from the software modules 102 to be processed by the hardwareinterface program 100 is likewise stored in the databases 98. Input datafrom the input functions 104 and input/output functions 106 is stored inan inputs database 116 and accessed by the hardware interface program100 as necessary.

As apparent to those of ordinary skill, the communications manager 96 ofFIG. 7 is logically arranged so as to separate the time base of thehardware interface program 100 and the software modules 102. Thedistributed databases 98 are the intermediaries that permit the hardwareinterface program 100 and software modules 102 to each operate withintheir own timing constraints. This separation may promote scalabilitybetween software and hardware comprising the EAS 10 illustrated in FIG.1.

Referring now to FIG. 8, another embodiment of a communications manager118, e.g., an intelligent cross-bar system, etc., includes a centralizeddatabase 120 that logically interfaces with a set of rules 122. Therules 122 govern how data is to be written to and retrieved from thedatabase 120 by various EAS application threads 123 n (123 a, 123 b, 123c, etc.) associated with various EAS applications 124, 126, 128, etc. Inthis example, the EAS application 124 may represent one or more of thesoftware modules 66 illustrated in FIG. 6 and elsewhere herein. The EASapplication 126 may represent another one or more of the softwaremodules illustrated in FIG. 6 and elsewhere herein, etc.

Threads 123 n are established with the EAS applications 124, 126, 128,etc., when communication of data between them is required. For example,the communications manager 118 may establish a thread 123 a with the EASapplication 124 to permit it to write data to the database 120 that willbe later used by the EAS application 126.

The data is assembled into protocol and communicated via a socketbetween the EAS application 124 and the communications manager 118. Therules 122 parse the data and assign it to its appropriate location inthe database 120 depending upon, for example, the nature of the data andwhich application produced the data. An appropriate set of other threads123 n are then invoked to transmit the data via, for example, the EASprotocol to their associated application. For example, a thread 123 b isestablished to facilitate the communication between the EAS application126 and the communications manager 118.

The EAS application 126 submits a request, via a socket, for the data.The rules 122 parse the request and provide the requested data thread123 b and thus the EAS application 126 via the socket.

As mentioned above, the avatar may convey information to the occupant 12illustrated in FIG. 1 and/or facilitate spoken dialog with the occupant12 through the use of simulated emotion. This simulated emotion may beexpressed visually by the avatar and/or audibly, for example, by thespeakers 62 illustrated in FIG. 5. Techniques to generate simulatedemotion are described with reference to FIGS. 9A and 9B.

Referring now to FIG. 9A, an emotion generator 132 receives a collectionof inputs from various modules described herein, analyzes/transformsthem and produces a simulated emotional state for the avatar. Thissimulated emotional state, in the embodiment of FIG. 9A, is in the formof a weighted emotional vector.

The simulated emotional state is communicated to and rendered by theavatar controller 92 illustrated in FIG. 6. As discussed below, therelative weighting of each variable of the emotional vector instructsthe avatar controller 92 as to the manner in which the avatar shouldappear and speak to express the appropriate emotion(s).

In the embodiment of FIG. 9A, the emotion generator 132 is implementedin software. The emotion generator 132, however, may be implemented infirmware or any other suitable configuration.

An emotion, such as fear, may be associated with a particular set ofavatar facial positions and speech patterns/tones that would berecognized as an expression of fear. Returning again to FIG. 6, theavatar controller 92 transforms the emotional vector, i.e., avataremotion, generated by the emotion generator 132 illustrated in FIG. 9Ainto a set of movements and facial expressions indicative of theemotion(s) to be expressed. The avatar controller 92, for example, mayinclude a database that transforms, e.g., maps, the range of weightedvalues for each emotion with a set of corresponding facial expressions:an avatar emotion of “happy” may correspond to lip positions indicativeof a smile; an avatar emotion of “happy” and “surprised” may correspondto lip positions indicative of a smile and eyebrow positions that areraised. The degree to which the avatar is smiling and/or raising itseyebrows, in this example, is a function of the weighting variableassociated with the emotion. The more heavily weighted the “surprised”emotion, the higher the eyebrow position, etc. For example, if theemotional vector is weighted to 50% “surprise,” and 50% “fear,” theavatar will appear (and speak) in a manner that suggests it is surprisedand afraid.

Several systems and/or algorithms may be used to determine thecorrespondence between facial expressions and emotions. For example,there is a long tradition of fine art that has codified the relationshipbetween expression and emotion. In addition there are codifications thatcorrespond to a scientific approach such as the Facial Action CodingSystem. Animators have developed a variety of packaged systems forputting emotion into pre-rendered animated characters such as the FacialAnimation Toolset for Maya and the Intel Facial Animation Library. Therelationship between different emotions, however, may be more complex.“Fear,” for example, activates specific sets of muscles in the face, asdoes “surprise.” To the extent the two sets are separate, the motionsthey produce are separate. Two emotions, however, may activate the samemuscles and, to the extent this is the case, those motions may becompromised/blended.

Referring now to FIG. 9B, the outputs of the automatic speechrecognition/voice recognition module 84 illustrated in FIG. 6 may bepre-processed by one or more of a prosodic analysis module 134, lexicalanalysis module 136 and/or syntactic analysis module 138. The outputs ofthe modules 134, 136, 138 are provided to an emotion estimator module140. The emotion estimator module 140 aggregates these outputs toproduce an estimation of the emotional state of the occupant 12illustrated in FIG. 1. In the embodiment of FIG. 9B, the modules 134,136, 138, 140 are implemented in software. These modules, however, maybe implemented in any suitable fashion.

The prosodic analysis module 134 of FIG. 9B may use multi-parametricspeech analysis algorithms to determine the occupant's affective state.For example, the specific features of the speech input, such as speechrate, pitch, pitch change rate, pitch variation, Teager energy operator,intensity, intensity change, articulation, phonology, voice quality,harmonics to noise ratio, or other speech characteristics, are computed.The change in these values compared with baseline values is used asinput into a classifier algorithm which determines the emotion on eithera continuous scale or as speech categories.

Prosodic analysis algorithms may be made more powerful if combined withsemantic analysis. These algorithms analyze the prosody of theoccupant's speech. For example, a rule may be implemented that maps avolume of speech with the emotion “excitement”: The greater the volume,the higher the rating of “excitement.” Other rules, of course, may alsobe implemented. Basic emotions may include “fear,” “anger,” “sadness,”“happiness” and “disgust.” Voice factors that may be indicative of theseemotions may include speech rate, average pitch, pitch range, intensity,timbre and articulation.

Lexical analysis of the speech may also be helpful: use of a word suchas “overcast” when “cloudy” would be more common could indicate anegative emotion. Further, syntax may be analyzed to determine, forexample, if the speaker uses the passive or active voice. Use of theactive voice may indicate confidence and happiness. Other techniquesand/or algorithms, etc., however, are also possible.

The lexical and syntactic analysis modules 136, 138 each apply a set ofalgorithms implemented, in certain embodiments, as rules to the speechrecognition outputs to generate respective emotional vectors indicativeof an assessed emotional state of the occupant 12 illustrated in FIG. 1.Lexical analysis algorithms may extract the text form of the wordsuttered by the occupant and classify them using an affective lexicon.One such lexicon is the Dictionary of Affective Language (DAL) thatcontains words of unambiguous emotional content. Statistical analysismay be applied to all the words in a corpus with unambiguous emotionalcontent to determine the emotion the speaker wishes to express. Forexample, the lexical analysis module 136 may map the frequency of theuse of expletives by the occupant 12 with the emotion “frustration”: Thegreater the frequency, the higher the rating of “frustration.”Algorithms implemented, in certain embodiments, as rules in thesyntactic analysis module 138 may map an average word length of spokensentences with the emotion “anger”: the shorter the average sentence,the higher the rating of “anger.” Other algorithms and/or rules, etc.may also be implemented. Syntactic analysis algorithms may use factorsin the spoken speech such as sentence length, use of punctuation, verbclass (experience or action), verb evaluation (positive or negative),verb potency (high or low), etc. to determine the emotion of thespeaker.

In certain embodiments discussed herein, four emotions are used torepresent the assessed emotional state of the occupant 12 illustrated inFIG. 1: “happy,” “sad,” “fear” and “surprise.” Other and/or differentemotions, however, may be used. These four emotions may be representedby three variables: “HS,” “FR,” and “SP.” The “HS” variable, forexample, may take on the values negative high (“NH”), negative low(“NL”), neutral (“NT”), positive low (“PL”) and positive high (“PH”).“NH” and “NL” are indicative of the degree of “sad.” “PL” and “PH” areindicative of the degree of “happy.” The “FR” and “SP” variables mayeach take on the values neutral (NT), positive low (PL) and positivehigh (PH).

The emotion estimator 140 of FIG. 9B applies algorithms implemented as aset of rules to the emotional vectors output by the modules 134, 136,138 and transforms them into an estimate of the emotion of the occupant12 illustrated in FIG. 1. Other suitable algorithms and/or analyticaltechniques, such as neural networks, may also be used. A set of fuzzybased rules, for example, may be applied to each of the modules 134,136, 138 assessment of “fear,” i.e., the “FR” variable, to reach anaggregate measure of “fear.” For example, if the respective measures of“fear” from each of the modules 134, 136, 138 are “HP,” “LP” and “NT,”then the fuzzy rules applied by the emotion estimator 140 may yield anaggregate measure of “fear” for the occupant 12 as “LP.” The measure of“fear” from each of the modules 134, 136, 138, in this example, is thuseffectively equally weighted.

In some embodiments, the algorithms applied by the emotion estimator 140may bias the results in favor of certain of the modules 134, 136, 138depending upon, for example, the accuracy and precision with which eachmeasures the emotional state of the occupant 12. In other embodiments,the emotion estimator 140 may dynamically bias the results in favor ofcertain of the modules 134, 136, 138 based upon feedback from theoccupant 12 illustrated in FIG. 1. The EAS 10 illustrated in FIG. 1 may,for example, occasionally ask the occupant 12 to describe theiremotional state in terms of “happy,” “sad,” “fear” and “surprise.” Uponreceiving such feedback from the occupant 12, the emotion generator 140may be tuned so that its results more closely track the occupant's ownassessment of their emotional state. If, for example, the lexicalanalysis module 136 is producing an assessed emotional state that mostclosely resembles the occupant's own assessment of their emotionalstate, the emotion generator 140 may begin to ignore or rely less on theemotional assessments from the prosodic and syntactic modules 134, 138.

Similar evaluations to those discussed above may be performed for othervariables of the emotional vectors from each of the modules 134, 136,138. Collectively, these variables form an emotional vector thatrepresents an estimated occupant emotional state based upon the speechof the occupant 12 illustrated in FIG. 1. As discussed below, thisestimated occupant emotional state may be used as an input indetermining the appropriate simulated emotional state for the avatar.

Returning again to FIG. 9A, occupant emotion from the emotion estimator140 illustrated in FIG. 9B, image recognition outputs from the imagerecognition module 82 illustrated in FIG. 6, vehicle systems outputsfrom the CAN bus interface 88 illustrated in FIG. 6 and agent emotionmay be provided as inputs to the emotion generator 132.

As discussed in more detail below, agents may be independent programsthat interact with the EAS 10 illustrated in FIG. 1 to implementspecific tasks/functions. In the embodiment of FIG. 9A, agent emotionmay be output by the agent(s) and indicate the quality with which theagent(s) is executing a task or an issue with the state of the vehicle14 illustrated in FIG. 1. For example, if the engine (not shown) is lowon oil, the avatar may reflect this with a negative expression.Likewise, a web agent that is responsible for establishing andmaintaining a wireless communication link with remote locations mayoutput an emotion that is a measure of the connectivity and performanceassociated with the communication link.

The following is an example algorithm implemented as a set of rules fortransforming the connectivity and performance associated with the webagent discussed above into a set of emotions. As apparent to those ofordinary skill, this example may also be illustrative of other types ofalgorithms discussed herein. In this example, the connectivity (“Conn”)of the computer 20 illustrated in FIG. 1 with information sourcesaccessible via the remote network 17 illustrated in FIG. 1 ischaracterized as either “Poor” or “Good.” The performance (“Perf”)associated with the connectivity is characterized as “Low,” “Medium” or“High.” Changes in the connectivity (“ConnChng”) and performance(“PerfChng”) are characterized as positive (“Pos”), neutral (“Zero”) ornegative (“Neg”):

-   1. If (Conn is Poor) and (ConnChng is Pos) then (HS is NL)(FR is    PL)(SP is PL).-   2. If (Conn is Poor) and (ConnChng is Zero) and (Perf is Low) and    (PerfChng is Zero) then (HS is NL)(FR is PL)(SP is NT).-   3. If (Conn is Good) and (ConnChng is Zero) and (Perf is Low) and    (PerfChng is Neg) then (HS is NL)(FR is PL)(SP is NT).-   4. If (Conn is Good) and (ConnChng is Zero) and (Perf is Low) and    (PerfChng is Zero) then (HS is NL)(FR is PL)(SP is NT).-   5. If (Conn is Good) and (ConnChng is Zero) and (Perf is Low) and    (PerfChng is Pos) then (HS is NT)(FR is NT)(SP is NT).-   6. If (Conn is Good) and (ConnChng is Zero) and (Perf is Medium) and    (PerfChng is Neg) then (HS is NT)(FR is NT)(SP is NT).-   7. If (Conn is Good) and (ConnChng is Zero) and (Perf is Medium) and    (PerfChng is Zero) then (HS is PH)(FR is NT)(SP is NT).-   8. If (Conn is Good) and (ConnChng is Zero) and (Perf is Medium) and    (PerfChng is Pos) then (HS is PL)(FR is NT)(SP is NT).-   9. If (Conn is Good) and (ConnChng is Zero) and (Perf is High) and    (PerfChng is Zero) then (HS is PL)(FR is NT)(SP is NT).-   10. If (Conn is Good) and (ConnChng is Zero) and (Perf is High) and    (PerfChng is Pos) then (HS is PL)(FR is NT)(SP is NT).-   11. If (Conn is Good) and (ConnChng is Neg) and (Perf is Low) and    (PerfChng is Neg) then (HS is NL)(FR is PL)(SP is NT).-   12. If (Conn is Good) and (ConnChng is Neg) and (Perf is Low) and    (PerfChng is Zero) then (HS is NT)(FR is PL)(SP is NT).-   13. If (Conn is Good) and (ConnChng is Neg) and (Perf is Low) and    (PerfChng is Pos) then (HS is NT)(FR is NT)(SP is NT).-   14. If (Conn is Good) and (ConnChng is Neg) and (Perf is Medium) and    (PerfChng is Neg) then (HS is NT)(FR is NT)(SP is PL).-   15. If (Conn is Good) and (ConnChng is Neg) and (Perf is Medium) and    (PerfChng is Zero) then (HS is PL)(FR is NT)(SP is PL).-   16. If (Conn is Good) and (ConnChng is Neg) and (Perf is Medium) and    (PerfChng is Pos) then (HS is PL)(FR is NT)(SP is NT).-   17. If (Conn is Good) and (ConnChng is Neg) and (Perf is High) and    (PerfChng is Neg) then (HS is PL)(FR is PL)(SP is PL).-   18. If (Conn is Good) and (ConnChng is Neg) and (Perf is High) and    (PerfChng is Zero) then (HS is PL)(FR is PL)(SP is NT).-   19. If (Conn is Good) and (ConnChng is Neg) and (Perf is High) and    (PerfChng is Pos) then (HS is PL)(FR is NT)(SP is PL).    The first rule indicates that if the connectivity is poor and the    change in connectivity is positive, then “happy/unhappy” is low    negative, “fear” is low positive and “surprise” is low positive. The    other rules may be interpreted in a similar fashion.

One or more of the above inputs may be used by the emotion generator 132to generate the avatar emotion. As an example, the emotion generator 132may ignore all but the vehicle systems outputs during vehicle operationso that the emotion expressed by the avatar is effectively a vehiclegauge. A parameter indicative of a position of an accelerator pedal ofthe vehicle 14 illustrated in FIG. 1 may, for example, be mapped with aneyebrow angle of the avatar. When accelerating, the avatar may displayan aggressive expression with its eyebrows angled down. Whendecelerating, the avatar may display a relaxed expression with itseyebrows angled up. Similarly, available power to the occupant 12illustrated in FIG. 1 may be mapped with a mouth curvature of theavatar. When the available power is greater than the requested power,the avatar may display a happy expression with its mouth showing asmile. When the available power is less than the requested power, theavatar may display an unhappy expression with its mouth showing a frown.Other configurations are, of course, also possible. As another example,the emotion generator 132 may ignore all but the speech and imagerecognition outputs if, for example, a non-driver occupant (not shown)is engaged in a conversation with the avatar. In this exampleconfiguration, the avatar does not convey vehicle related information tothe non-driver occupant. Other configurations and arrangements are alsopossible.

This selective capability of the emotion generator 132 may reflectmotivation and intent on the part of the EAS 10 illustrated in FIG. 1.For example, the EAS 10 may use simulated emotion to convey the urgencyof what is being said, to appeal to the occupant's emotions, to conveythe state of the vehicle systems 22 illustrated in FIG. 1, etc. The EAS10 may thus determine the appropriate times to display emotionsindicative of various inputs.

The selective capability discussed above may be implemented throughoccupant request and/or automatically. In some embodiments, the occupant12 illustrated in FIG. 1 may instruct the EAS 10 to ignore all butvehicle information, e.g., vehicle systems outputs, (or other inputsillustrated in FIG. 9A) when generating its emotion. In otherembodiments, the EAS 10 may automatically ignore all but vehicleinformation during vehicle operation if, for example, the EAS 10 intendsto emphasize the state of the vehicle 14 illustrated in FIG. 1 whilecommunicating with the occupant 12. Algorithms may direct the EAS 10 todo this if, for example, certain vehicle operating parameters, such astire pressure, fuel levels, engine temperature, etc., reach criticallevels. Such an algorithm may provide that if the engine temperature is“hot”, ignore all but vehicle systems outputs. In still otherembodiments, the EAS 10 may automatically ignore all but the imagerecognition outputs and occupant emotion if, for example, the EASencounters a new driver and is attempting to establish an emotional bondwith this new driver. Other arrangements are also possible.

The emotion generator 132 may apply one or more algorithms, implementedin the embodiment of FIG. 9A, as a set of rules, similar to thosediscussed with reference to the emotion estimator 140 illustrated inFIG. 9B, to aggregate the inputs and generate the simulated emotionalstate for the avatar, i.e., avatar emotion. This emotional state takesthe form of a weighted multi-variable vector, i.e., emotional vector. Asdiscussed above, this emotional vector may include variables indicativeof the emotions “happy,” “sad,” “surprise” and “fear”(“excitement-quiescence,” “pleasant-unpleasant,” etc.) Each variable mayinclude an associated weighting value to indicate the degree with whichthat particular emotion is to be expressed. As discussed above, however,other techniques may be used to produce the emotional state for theavatar. For example, a suitable neural network may be provided thataggregates the various inputs received by the emotional generator 132into the simulated emotional state of the avatar.

As discussed above, the EAS 10 illustrated in FIG. 1 may engage inconversation with the occupant 12 also illustrated in FIG. 1 to gatherinformation from the occupant 12 and/or provide information to theoccupant 12. Algorithms/techniques/methods used to manage and facilitatethis conversation are discussed with reference to FIGS. 10 though 12.

Referring now to FIG. 10, a spoken dialog manager 142 receives inputsoriginating with the occupant 12 illustrated in FIG. 1, e.g., imagerecognition, speech recognition, button press, occupant emotion, as wellas inputs originating with agents, e.g., agent initiated tasks. Thespoken dialog manager 142 processes these inputs and generates tasks,for example, for the avatar, agents and/or vehicle systems 22illustrated in FIG. 4.

The spoken dialog manager 142 of FIG. 10 may be implemented as softwareusing a logic programming language such as PROLOG, Datalog, HiLog,λProlog, etc. These languages may be associated with computationallinguistics. Of course, other high level languages, such as Java, LISP,etc., may also be used. In other embodiments, the spoken dialog manager142 may be implemented on embedded processors, field programmable gatearrays, web-servers, etc.

The tasks generated by the spoken dialog system 142 may comprise textfor the avatar to speak, the meaning the spoken dialog manager 142wishes to convey, an event that will trigger text to be spoken, apriority for a given text to be spoken, the nature of how a currentavatar operation should be interrupted (conveying urgency to theoccupant), an emotion, an action for an agent, a priority for an actionand an event that will trigger the execution of an action, etc.

The spoken dialog system 142 may generate content for a particular taskbased upon algorithms, implemented in the embodiment of FIG. 10, as aseries of rules used to interpret the occupant and/or agent input withinthe given context. For example, a rule may provide that a downward gazeof at least 20 seconds will result in a task being generated that willremind the driver to keep their eyes on the road. The text and priorityassociated with such a task may be “Keep your eyes on the road!” and“High” respectively. The high priority of the task will cause the avatarto interrupt between words, for example, and abort any current task toconvey the urgency needed to ensure the occupant is alerted. In thisexample, the task does not include an action for an agent as no agentsare involved in the execution of this task. The task also does notinclude a triggering event because the task is intended to be performedimmediately. Another rule may provide that a request from the occupantto “Put the vehicle in fuel economy mode.” will result in a task beinggenerated that will alter the appropriate engine tuning parameters tomake the engine more fuel efficient. Assuming that such altering ofengine tuning parameters must take place while the engine (not shown) isidling, the text and priority associated with such a task may be “I amputting the engine in fuel economy mode.” and “Medium” respectively. Theaction may be directed to a powertrain agent and will include theappropriate instructions that will permit the agent to alter the desiredparameters. The triggering event may be the engine at idle for at least3 seconds. Still yet another rule may provide that any agent initiatedtask, discussed in more detail below, will result in a task beinggenerated that will ask the occupant 12 illustrated in FIG. 1 whether itis acceptable to perform the task if the occupant emotion is “unhappy.”The text and priority associated with such a task may be “I don't wantto bother you, but the X agent recommends that I do Y. Is that O.K.?”and “Low” respectively. Other and/or different rules may also beimplemented.

Referring now to FIG. 11, the algorithms/rules discussed above may beimplemented in a task generator 144 as software or firmware. Othersuitable alternatives, however, are also contemplated.

The task generator 144 of FIG. 11 serially queues the multiple inputs,e.g., speech recognition, occupant emotion, etc., and processes them togenerate tasks. Any text to be spoken by the avatar is created withinthe task generator 144 of FIG. 11 by selecting text from a set ofpre-programmed statements, an agent, or may be synthesized usingoptimality theory techniques. The text may also be produced in anabstract “meaning language” like First Order Predicate Calculus suchthat an emotional speech synthesizer, discussed below, may create theemotionally tagged text. Any actions that need to be performed areselected from a list of available actions. Actions may be made availableby agents or modules, e.g., plug-n-play modules, discussed herein. Apriority and any triggering event for the task is determined by rules,such as those described above. These components of the task areassembled into an EAS protocol message that is sent to a task manager,discussed in more detail below. Of course, any suitable protocol, suchas XML, may be used.

As illustrated, the agent initiated tasks may be classified intohigh-priority and low-priority tasks. In some embodiments, thisclassification may be assigned by the agent generating the task usingtechniques similar to those described above with reference to the taskgenerator 144. For example, algorithms associated with a safety agentthat, inter alia, monitors the wheel slip and speed of the vehicle 14may assign a high-priority to a task it generates indicative of arequest to the driver to slow down because the road is slippery. Inother embodiments, this classification may be assigned by the spokendialog manager 142. For example, a task from a news agent that monitorsvarious data sources accessible via the network 17 illustrated in FIG. 1for news of interest to the occupant 12 also illustrated in FIG. 1 maybe assigned a low-priority by the spoken dialog manager 142. Otherconfigurations are also possible.

Agent tasks have a similar nature to button tasks and speech tasks inthat they may alter the context of the dialog between the occupant andthe EAS illustrated in FIG. 1.

As mentioned above, the EAS 10 illustrated in FIG. 1 includes openmicrophone functionality. An open microphone 146 facilitates the abilityof the EAS 10 to receive instructions from the occupant 12 illustratedin FIG. 1 without the occupant 12 having to, for example, press abutton. This open microphone system works in some embodiments because,as described above, the EAS 10 may be able to determine the number ofoccupants in the vehicle 14 illustrated in FIG. 1 and the location ofthese occupants using the OCS discussed above, to determine the locationof the speaker using acoustics, to determine if the utterance itreceives is in context, to determine if the occupant 12 is looking atthe avatar using gaze detection, to remove the avatar's voice from theacoustic signal from the microphone using sound cancellation, etc. Theopen microphone may also be used with “barge-in” where the occupant caninterrupt the avatar when necessary.

During an initial state entered, for example, upon vehicle start-up,algorithms implemented by the open microphone 146 listen for at leastone of a limited number of words/statements, e.g., the name of theavatar, etc. Once detected, the open microphone 146 transitions into aconversational mode that allows it to accept a larger set ofwords/statements. As such, this transition is triggered, in thisexample, only by the voice of the occupant 12 illustrated in FIG. 1.

The larger set of words/statements that may be accepted in theconversational mode may be restricted by a context of the conversation.For example, statements made by the occupant 12 illustrated in FIG. 1outside a current context may be ignored by the open microphone 146. Inother embodiments, image recognition information, speech prosody, etc.,may also be used to determine whether the speech is directed to the EAS10 illustrated in FIG. 1. For example, if the occupant 12 is looking atanother occupant (not shown) and speaking, the speech is likely notdirected to the EAS 10. Likewise, if the speech of the occupant 12 isindicative of singing, such singing is likely not directed to the EAS10. Therefore, the EAS 10 may be capable of determining whether it is alistener or an addressee of the occupant's speech.

Referring now to FIG. 12, algorithms implemented by the task generator144 evaluate inputs from each of the image recognition, speechrecognition, button press, occupant emotion and agents in a serialfashion. That is, as the various types of inputs are received, they areblocked and evaluated sequentially, or in a “round robin” fashion, bythe task generator 144. To enable this blocking function, the spokendialog manager 142 includes respective write commands 150, 152, 154,156, 158 for each of the types of input.

In other embodiments, algorithms implemented by the task generator 144may evaluate inputs from each of the image recognition, speechrecognition, button press, occupant emotion and agents, etc. based ontime stamps associated with each input. These time stamps may begenerated, for example, by any of the software modules 66 illustrated inFIG. 6 or elsewhere described herein. A time stamp may be determined bythe state of the system clock (not shown) when the data is received. Thetask generator 144 sorts received inputs by their respective time stampsinto a queue. Once queued, the task generator 144 may evaluate them asdescribed above. Generally speaking, this an a application for a queuingalgorithm such as Fair Queueing, Weighted Fair Queueing, Token Bucket,Round Robin, etc.

As apparent to those of ordinary skill, the speech recognition inputs ofFIG. 12 require additional processing prior to being written to the taskgenerator 144. As indicated at 160, the spoken dialog manager 142translates any recognized speech in the form of text into a recognition,i.e., a set of hypotheses and an utterance recording associated with therecognized speech, using any suitable speech recognition engine, such asNuance Recognizer, Nuance VoCon, SRI International DECIPHER, MITJupiter, etc. The spoken dialog manager 142 may then apply any one ormore of a context, gaze direction, etc. to determine if the recognitionis an input as indicated at 162.

In the embodiment of FIG. 12, the application of context via, forexample, a finite state machine to the recognition may be performed todetermine whether the occupant 12 illustrated in FIG. 1 is attempting tocommunicate with the EAS 10 or, for example, another occupant of thevehicle 14 illustrated in FIG. 1. If, for example, the recognitioncomprises “get me the news,” the spoken dialog manager 142 may determinethat the recognition is input, i.e., that the dialog is directed towardthe EAS 10 illustrated in FIG. 1 and not, for example, another occupantin the vehicle 14. If, on the other hand, the recognition comprises “Hi,mom,” the spoken dialog manager 142 may determine that such speech isnot directed to it and return to 160.

As indicated at 164, the spoken dialog manager 142 then selects the bestsentence from the recognition alternatives. For example, if a currentconversation with the occupant 12 illustrated in FIG. 1 is regardinglocal restaurants, i.e., the spoken dialog manager 142 is in therestaurant context, it may select the “get me the reviews” phrase. Itmay be more probable that the occupant 12 would request reviews withinthe context of a conversation about restaurants as opposed to a requestregarding the news.

As discussed above, the EAS 10 illustrated in FIG. 1 may convey asimulated emotional state. Algorithms/technologies/methods, etc. tofacilitate such an emotional state are described with reference to FIGS.13 through 15.

Referring now to FIG. 13, an emotional speech synthesizer 164 combinesthe avatar emotion and avatar prompt, i.e., text for the avatar tospeak, into emotively tagged text. As discussed above, in certainembodiments the avatar emotion has a vector representation. This vectorrepresentation is used by the emotional speech synthesizer 164 of FIG.13 to mark-up portions of the text to be spoken by the avatar withindicators of emotion. These emotional markers are later interpreted bya text to speech engine, discussed below, in order to dynamically alterthe prosody, tone, inflection, etc., with which the marked words arespoken. Such dynamic alteration of word pronunciation may conveyemotional content in the speech.

If, for example, the avatar prompt includes the text “Have a nice day”and the avatar emotion is indicative of “calm,” the emotional speechsynthesizer 164 may output the emotively tagged text: “<speechstyleemotion=“calm”> Have </speechstyle> a great day.” Syntactically, theword “Have” is surrounded by the markers “<speechstyle emotion=calm>”and “</speechstyle.>” This designation signals the text to speech enginethat the word “Have” has emotional content associated with it. (Thephrase “a great day” does not have such content and will, as a result,be spoken in a neutral fashion.) Of course, other syntax schemes may beused. In this example, the word “Have” is marked to be spoken in a“calm” manner. As discussed below in detail, rules implemented in thetext to speech engine may translate the emotive marker “calm” into a setof associated speed, pitch, pitch change, volume, high frequencycontent, etc., that will affect they way in which the word “Have” isspoken.

Other speech markers are defined in the Speech Synthesis Markup Languagespecification from the World Wide Web Consortium (W3C). Prosodic andemphasis elements that indicate avatar emotion are of the form “Have a<emphasis> nice </emphasis> day!” which would put the stress on the wordnice. Other elements that may be implemented similarly are: the breakelement that may be used to simulate different articulation and pausesin the speech; and the prosody elements pitch, pitch contour, pitchrange, speech rate, speech duration and speech volume (intensity). Theability to use these elements may be limited by the text to speech (TTS)technology used. The TTS may be a computer program and may typically useconcatenative, articulation modeling, formant synthesis ordomain-specific synthesis, etc. It may also be a mechanical device thatis acoustically similar to the human vocal tract.

The choice of speech synthesizer may have an impact on the naturalnessof the voice which in turn impacts the actual words chosen for thespeech. If the voice sounds mechanical like it comes from a computer,the use of words such as “I” and “me” should be limited. If the computervoice is very natural, as in the case of domain-specific synthesis, “I”and “me” may be used more readily.

The mechanical nature of the voice may be part of the persona of the EAS10 illustrated in FIG. 1 which may also be linked to the appearance ofthe avatar. The emotional speech synthesizer 164 may have the ability toreplace the phraseology of the text to reflect passive-active voice,introspection-extrospection, active emotional state-passive emotionalstate, positive or negative emotional valance, anger, rage, frustration,happiness, etc. Thus the emotional speech synthesizer 164 may be capableof replacing words and syntax, and inserting the correct prosody for aparticular avatar emotion, weaving the explicit meaning of the text withthe implicit emotion that should also be conveyed.

In certain embodiments, algorithms associated with the emotional speechsynthesizer 164 are implemented in a finite state transducer thataccepts the non-emotional speech as a lexical tape and the emotionalstate of the avatar as inputs. It first processes the input usingsemantic analysis, to create a meaning representation of the text.Meaning representations may take several forms, such as First OrderPredicate Calculus, Semantic Networks, Conceptual Dependency Diagrams,frame-based representations, etc. This yields a representation of theliteral meaning of the sentences. The emotional speech synthesizer 164may include a catalog of emotional tagged sentences that the avatar canmake and for which the literal meanings and the emotion have beencomputed. The emotional speech synthesizer 164 matches the literalmeaning of the cataloged sentences and the current avatar emotion toselect emotionally tagged sentences to be sent to the avatar. Theemotional speech synthesizer 164 may also generate new sentences basedon synonym substitution and using techniques such as those of optimalitytheory.

Algorithms implemented in the emotional speech synthesizer 164 may alsomodify the pronunciation of words (such as changing the pronunciation ofthe word “the” to either “thee” or “th[schwa]” and set accented syllableusing allophonic transcription rules that incorporate the emotion of theavatar. The allophones created in this process may be represented by aallophonic alphabet such as the International Phonetic Alphabet (IPA),ARPAbet, etc. Dictionaries of pronunciation such as PRONLEX, CMUdict,CELEX, etc. are also widely available.

Algorithms implemented in the emotional speech synthesizer 164 may thentake the sentences created by the above processes and choose among thesentences created by the above processes that best fit rules of syntax,language orthography, emotion, etc., in addition to maintaininghistorical information about which sentences have been used in the pastto avoid repetition (which may become annoying over time). The selectedsentence may then be output from the emotional speech synthesizer 164for processing by the TTS.

Referring now to the algorithms of FIGS. 14A and 14B, the emotionalspeech synthesizer 164 waits for text from the avatar prompt asindicated at 166. As indicated at 168, when text is detected, theemotional synthesizer 164 gets the text and then, as indicated at 170,gets the avatar emotion. As indicated at 172, the emotional speechsynthesizer 164 embeds emotional tags in the text. As indicated at 174,the emotional speech synthesizer 164 outputs the emotively tagged text.

As indicated at 176, the first word of the text is parsed. Adetermination is made as to whether the parsed word is to have emotionalcontent as indicated at 178. In the embodiment of FIG. 14B, thisdetermination is based on the parsed word and the avatar emotion. Forexample, a database including key words mapped with emotions may beconsulted to determine whether a particular word is to have emotionalcontent. The database may include the word “have” associated with theemotions “calm” and “happy.” Rules implemented by the emotional speechsynthesizer 164 may indicate that if the parsed word is “have” and theavatar emotion is indicative of “calm” or “happy,” then the word “have”will be marked with emotion indicated by the avatar emotion. If,however, the avatar emotion is indicative of emotions other than “calm”or “happy,” then the word “have” will not be so marked. (Lexicalemotional analysis techniques, for example, may be used to determinewhich words in the database are to have emotional content.)

In embodiments implementing the above described scheme, a particularword may or may not have emotional content depending upon the emotionassociated with the avatar. In other embodiments, rules may beimplemented that direct the emotional speech synthesizer 164 to mark thefirst parsed word of the text with the emotion of the avatar, or, tomark the first verb encountered in the text with the emotion. Otherconfigurations and techniques are, of course, also possible.

If the parsed word is to be emotively tagged, the emotional speechsynthesizer 164 embeds the emotional tag with the parsed word asindicated at 180. As indicated at 182, the next word of the text isparsed. A determination is then made as to whether the end of the texthas been reached as indicated at 184. If yes, the process proceeds to174. If no, the process returns to 178.

Returning to 178, if the parsed word is not to be emotively tagged, theprocess proceeds to 182.

Referring now to FIG. 15, the avatar controller 92 may include arendering engine 184, e.g., Renderware, Torque Game Engine, TV3D, 3DGame Studio, C4 Engine, DX Studio, Crystal Space, Game Blender, etc.(some of these use several graphics oriented APIs including Direct3D,OpenGL, DirectX, SDL, OpenAL, etc.), and a text to speech engine 185,e.g., Nuance Recognizer, Nuance VoCon, SRI International DECIPHER, MITJupiter, etc., implemented in software. Of course, the engines 184, 185may be implemented in firmware, hardware, etc. as desired. As discussedabove, the rendering engine 184 renders appropriate virtual buttons forthe display 40 illustrated in FIG. 2 based on the button renderinginput. Likewise, the rendering engine 184 renders appropriate avatarmovements, e.g., hand movements, head movements, etc., for the display40 based on the avatar gestures input.

In the embodiment of FIG. 15, the rendering engine 184 receives theemotively tagged text and provides it to the text to speech engine 185.The text to speech engine 185 may then perform concatenative synthesisof the avatar's voice with an emotional speech database and postprocessing for prosody.

The text to speech engine 185 may include a database (not shown) ofallophones recorded with emotional voices, stored, for example, inLinear Predictive Coding (LPC) or cepstral form and indexed by theemotion as well as by the allophone itself. An entry in such a databasefor the emotion “calm” may dictate a set of prosody, tone, pitch, speed,etc. parameters that are applied to a word emotively tagged with theemotion “calm.” Another entry in the database for the emotion “sad” maydictate another set of prosody, tone, pitch, speed, etc. parameters thatare applied to a word emotively tagged with the emotion “sad.”

Algorithms implemented in the text to speech engine 185 may selectallophones from the database on the basis of the intended articulatedsound and the required prosody, decode the allophones adjusting theduration, pitch, pitch profile, etc., then concatenate the allophonesinto speech followed by digital signal processing that smoothes theboundaries of the allophones and adds other emotional prosody effectslike the rising pitch at the end of a sentence or an accent on aparticular syllable.

Avatar events are generated by the text to speech engine 185 andprovided to the rendering engine 184. In the embodiment of FIG. 15, theavatar events include visemes, syllables, words, sentences andparagraphs. Other and/or different avatar events may also be used. Thetext to speech engine 185 of FIG. 15 includes a mapping of phonemes withcorresponding visemes. An avatar event indicative of a viseme is sent tothe rendering engine 184 each time digital audio data indicative of aphoneme is output for the speakers driver 80 illustrated in FIG. 6.Likewise, an avatar event indicative of a word is sent to the renderingengine 184 each time digital audio data indicative of a word has beenoutput for the speakers driver 80, etc. As an example, digital audiodata indicative of the sentence “How are you?” would result in thefollowing stream of avatar events (assuming there are two visemesassociated with the word “How,” one viseme associated with the word“are” and two visemes associated with the word “you”): viseme, viseme,syllable, word, viseme, syllable, word, viseme, viseme, syllable, word,sentence.

The rendering engine 184 of FIG. 15 includes a mapping of visemes, etc.with corresponding lip positions. As apparent to those of ordinaryskill, a stream of avatar events may inform the rendering engine 184 asto the lip positions that correspond to the digital audio data outputfor the speakers driver 80 illustrated in FIG. 6.

As discussed in more detail below, the avatar events may be used as atiming basis to determine if/when to interrupt current speech of theavatar with speech of a more urgent nature. For example, avatar speechmay be interrupted on the next viseme, syllable, word, sentence,paragraph, etc. As such, the rendering engine 184 outputs avatar eventsreceived from the text to speech engine 185 to, inter alia, inform othermodules discussed herein as to the state of the speech associated withthe avatar.

As discussed above, the rendering engine 184 may translate the avataremotion into a set of facial expressions/colors/etc. corresponding tothe avatar emotion. The rendering engine 184 of FIG. 15 includes adatabase that maps emotions with facial positions. For example, thedatabase may include an entry that maps the emotion “angry” with thecolor red such that the avatar turns red when it is angry. The databasemay also include an entry the maps the emotion “envy” with the colorgreen such that the avatar turns green when it is envious. Similarly,positions of various features or the features themselves may be alteredwith emotion. Other configurations are also possible.

In other embodiments, the rendering engine 184 may interpret theemotively tagged text before providing it to the text to speech engine185 in order to determine how to alter the appearance of the avatar.Using a previous example, algorithms implemented in the rendering engine184 may interpret the emotively tagged text “<speechstyleemotion=“calm”> Have </speechstyle> a great day.” in order to determinethat the avatar is to convey the emotion “calm.” Of course, other and/ordifferent syntaxes for the emotively tagged text may be used tofacilitate interpretation by the rendering engine 184. For example, theavatar emotion may be appended onto the end of emotively tagged text.The rendering engine 184 may parse the text, remove the avatar emotionand provide the resulting tagged text to the text to speech engine 185.

As discussed above, the EAS 10 illustrated in FIG. 1 may learn toanticipate requests, commands and/or preferences of the occupant 12 alsoillustrated in FIG. 1 based on a history of interaction between theoccupant 12 and the EAS 10. Techniques/algorithms/methods, etc. toenable such learning are discussed with reference to FIGS. 16 through17C.

Referring now to FIG. 16, an EAS learning module 186, implemented insoftware, firmware, etc. receives occupant requests directed to the EAS10 illustrated in FIG. 1. The requests and associated conditions (someof which are illustrated in FIG. 6) under which the requests were madeare recorded in a database 188. For example, the learning module 186 mayrecord that, on four separate occasions, a driver set the cruise controlof the vehicle 14 illustrated in FIG. 1 after having traveled for atleast 1 minute at 60 miles an hour.

In some embodiments, the learning module 186 is an intelligent systemthat implements algorithms that first uses approximate reasoning todetermine what action is needed and then learns by: observing when theoccupant 12 illustrated in FIG. 1 selects a particular action;suggesting the occupant 12 take an action and learning from theoccupant's response; observing when the occupant 12 cancels an actionthe EAS 10 illustrated in FIG. 1 initiated automatically, etc.

As discussed above, the conditions recorded may be any of the speechrecognition outputs, image recognition outputs, button press, vehiclesystem outputs, etc. illustrated in FIG. 6. Other conditions, such asgeographic location, weather information, occupant emotion, avataremotion, etc., may also be recorded as desired.

As explained below, the learning module 186 compiles this request andcondition information to anticipate future requests and/or to filteragent generated tasks. Once compiled, the learning module 186 may createat least one task for the avatar and/or an agent based on a set ofrecognized conditions.

Continuing with the above example, the learning module 186 may provide arule that specifies that if the cruise control is set at least fourtimes while continuously holding a fixed speed on the highway (requiringinformation from the navigation system and wheel speed data processedusing statistical process control), an avatar task will be generated toask the driver if they would like the cruise control set once the speedof the vehicle 14 illustrated in FIG. 1 reaches 60 miles an hour. Inthis example, the learning module 186 now has a record of suchconditions and the record satisfies the rule. As a result, the learningmodule 186 may generate the task described using techniques describedherein. The spoken dialog manager 142 illustrated in FIG. 10 mayinterpret an affirmative response to such a query and, as a result,generate a task for a powertrain agent to implement cruise control.

The learning module 186 may record responses to requests similar tothose described in the above example to further learn from the occupant12 illustrated in FIG. 1. Still continuing with the above example, thelearning module 186 may further provide a rule that specifies that ifthe driver affirmatively responds to such queries three times, thecruise control should automatically be set and the driver informed suchis being done. Likewise, the learning module 186 may also provide a rulethat specifies that if the driver negatively responds to such requeststwo times, the driver should no longer be queried regarding the cruisecontrol for a period of 2 weeks. Other and/or different rules may, ofcourse, be implemented within the learning module 186.

In other embodiments, the database 188 may include a set ofpre-specified conditions and associated tasks. Each time a task isimplemented, the pre-specified conditions are checked. If thepre-specified conditions are met, a counter is incremented. Once thecounter achieves a threshold value, a rule, for example, may specifythat the next time the conditions occur, the learning module 186 is togenerate a task to query the occupant 12 illustrated in FIG. 1 as towhether they wish the task to be completed or to complete the task andinform the occupant 12 that the task was completed, etc. As an example,the database 188 may include a first task entry of “set cruise control”and an associated condition of “speed greater than 60 miles per hour.”The database 188 may also include a second task entry of “set cruisecontrol” and an associated condition of “speed greater than 10 miles perhour and less than 20 miles per hour.” A counter is also provided witheach of these entries. If the cruise control is set on three separateoccasions at speeds respectively of 65 miles per hour, 68 miles per hourand 60 miles per hour, the counter associated with the first task entrywill have been incremented three times while the counter associated withthe second task entry will not have been incremented. Assuming athreshold value of three, the next time the speed of the vehicle 14illustrated in FIG. 1 exceeds 60 miles per hour, a rule implemented inthe learning module 186 may trigger the generation of a task, using thetechniques described herein, that will prompt the EAS 10 illustrated inFIG. 1 to query the occupant 12 as to whether they wish the cruisecontrol to be set. As another example, the database 188 may include atask entry of “turn on classical music” and an associated condition of“occupant emotion=angry.” If the occupant 12 plays classical music, forexample, on four separate occasions when the occupant emotion is“angry,” the next time the occupant emotion is “angry,” the EAS 10 mayplay classical music (or ask the occupant 12 if they would like to hearclassical music if the rule permits.) Of course, other EAS behaviors,vehicle operating parameters, etc. may also be altered in a mannersimilar to that described above.

In still other embodiments, analytical techniques may be used togenerate rules that permit the learning module 186 to learn from theoccupant 12 illustrated in FIG. 1. For example, the learning module 186may implement a neural network that monitors the condition inputs andattempts to match patterns of conditions with occupant requests. Such aneural network may recognize, for example, that at a particular time ofday, a certain driver asks for news about financial markets. As aresult, the learning module 186 may generate a task for an agent togather such news about financial markets in advance of the particulartime of day and generate a task to ask the driver just before thatparticular time of day if they would like the news about financialmarkets. In this example, the neural network may further recognize thatafter several negative responses to such learning module 186 initiatedrequests, the neural network may no longer gather such news or promptthe driver regarding such news in advance of the particular time of day.Likewise, several affirmative responses to such requests may reinforcethis behavior governed by the neural network. Other suitable techniques,however, may also be used.

As mentioned above, the learning module 186 may filter agent generatedtasks consistent with the compiled information from the database 188.For example, a fuel economy agent may be configured to prompt the avatarto ask, as a default, that the vehicle be put in fuel economy mode atengine start-up. Compiled information from the database 188 may reveal arecord of affirmative or negative responses to such requests. If, forexample, the learning module 186 compiles a set of mostly negativeresponses, the learning module 186 may terminate the fuel economy agentinitiated task. If, for example, the learning module 186 compiles a setof mostly affirmative responses, the learning module 186 may generate atask that automatically puts the vehicle 14 into fuel economy mode andmerely informs the driver that it is doing so.

The EAS 10 illustrated in FIG. 1 may also download its learnedpreferences from the learning module 186 to one or more of the servers16 n illustrated in FIG. 1. The one or more servers 16 n may aggregatethis information with other such information from other EAS. The EAS 10may then request this aggregated preference information topre-load/update the learning module 186 with the collective experienceof numerous EAS.

Referring now to the algorithm of FIG. 17A, a thread of the learningmodule 186 waits for an occupant request as indicated at 190. Asindicated at 192, the thread updates the database 188 with the instantconditions when an occupant request is received.

Referring now to the algorithm of FIG. 17B, another thread of thelearning module 186 waits for an update to the database 188 as indicatedat 194. As indicated a 196, the thread compiles occupant preferencesfrom the database 188 when then database 188 is updated.

Referring now to the algorithm of FIG. 17C, yet another thread of thelearning module 186 waits for a change in input conditions as indicatedat 198. As indicated at 200, when a change occurs, the thread comparesthe new conditions with any occupant preferences compiled previously at196. The thread then determines whether to initiate an action asindicated at 202. For example, the thread may determine that a currentset of conditions fulfill a particular rule similar to the rulesdiscussed above. If yes, the thread outputs a task as indicated at 204.The thread then returns to 198. If no, the thread then determineswhether to request occupant input as indicated at 206. For example, thethread may request such input if a rule specifies that a task for anaction may be initiated if the occupant provides an affirmative responseto an inquiry. If no, the thread returns to 198. If yes, the thread getsthe occupant response as indicated at 208. As indicated at 210, thethread updates the history database 188. The thread then determineswhether to initiate the task as indicated at 212. This determination maydepend upon, for example, whether the occupant 12 illustrated in FIG. 1provided an affirmative response. If yes, the thread proceeds to 204. Ifno, the thread returns to 198.

Various tasks generated by differing modules have been discussed above.Techniques/methods/algorithms, etc. that may be used to prioritize andexecute such tasks are discussed with reference to FIGS. 18 through 20.

Referring now to FIG. 18, a task manager 214 manages the resourcesassociated with the EAS 10 and vehicle 14 both illustrated in FIG. 1 anddemanded by the various tasks described above. In certain embodiments,the avatar may only engage in one task at a time. As such, algorithmsimplemented by the task manager 214 schedule and execute the tasksrelated to the use of the avatar based on a priority scheme. Thispriority scheme, inter alia, dictates whether a certain task may beperformed immediately, thus interrupting any current task, or may beperformed at some later time. As discussed below, the avatar may beinterrupted to perform a task or may begin a new task once a currenttask is complete. The task manager 214 thus balances the use of theavatar with the load placed upon it by the various actors within thesystem described herein.

The task manager 214 may receive EAS initiated tasks, occupant initiatedtasks and/or agent initiated tasks, etc. The task manager 214 thenqueues and executes them accordingly. In some embodiments, the taskmanager 214 queues each of the tasks based on its priority and executeseach of the tasks based on this priority. Because the task manager 214may interrupt a current task to execute a higher priority task, it mayterminate, suspend and/or resume the current task.

As discussed above, avatar events, e.g., visemes, syllables, words, etc.are received and used by the task manager 214 of FIG. 18 as a basis todetermine when to execute the queued tasks (provided that any triggeringevent(s), discussed below, have been met). For example, a high prioritytask may interrupt a current task upon a next syllable of the avatar. Amedium priority task may be performed at the end of a sentence of theavatar. A low priority task may be performed when there are no otherhigher priority tasks to be performed.

The execution of a task may involve one or more agents. For example, atask may involve a news agent that collects news from informationsources available via the web. Such agents may need to be interruptedif, for example, they require avatar resources. As a result, the taskmanager 214 of FIG. 18 may output agent instantiation, termination,suspension, resumption, etc. commands. The execution of a task mayfurther involve text for the avatar to speak. The task manager 214outputs such text via the avatar prompt.

Referring now to FIG. 19, a task queue 216 may be used to queue thetasks managed by the task manager 214 based on, for example, thepriority and any triggering events associated with the tasks. In theembodiment of FIG. 19, the triggering events are represented as variousbins within the task queue 216. For example, a task that lacks atriggering event may be binned as an “Immediate” task, e.g., “Task 1.” Atask that should be performed in the vicinity of a gas station may bebinned as a “Wait For Geographic Event,” e.g., “Task 2.” A task thatshould be performed when tire pressure is less than a certain value maybe binned as a “Wait For Vehicle Systems Event,” e.g., “Task 3.” Otherand/or different binning techniques may, of course, be used.

Within a particular bin, each task is ordered based upon a specifiedavatar event embedded with the task. As discussed above, avatar eventsmay include visemes, syllables, words, sentences, paragraphs, etc. Thosetasks that are to be performed upon the next viseme, e.g., “Task 4” willbe executed before tasks that are to be performed upon the nextparagraph, e.g., “Task 5.” Thus when generated, each task may includeinformation indicative of the avatar event that will trigger itsexecution.

As new tasks are received by the task manager 214, they are binnedwithin the task queue 216. Within each bin, the tasks are thenre-ordered, as necessary, depending upon the avatar event associatedwith each task.

Returning again to FIG. 18, tasks that are interrupted may be aborted,suspended or re-queued. A suspended task will continue from the point itwas interrupted after completion of higher priority tasks. For example,if a news reading task is suspended, it will begin reading the news fromwhere it left off when it was interrupted. A re-queued task will restartafter completion of higher priority tasks. For example, if the newsreading task is re-queued, it will start, when executed, from thebeginning of its news cast.

Aborting, suspending and re-queuing tasks may require the task manager214 to instantiate, terminate, suspend or resume one or more agents. Asdiscussed below, if a task requires an agent that is not instantiated,the task manager 214 may issue a command to instantiate an agent when,for example, a triggering event for the task occurs. Any current agentsconsuming avatar resources may need to be terminated or suspended if ahigher priority task is to be executed. The termination and suspensioncommands discussed above are issued by the task manager 214 under suchcircumstances. If an agent is suspended, it may later be resumed via aresumption command as discussed above.

Multiple tasks may be suspended under circumstances where multipleagents have yet to complete their tasks and also require avatarresources. In some embodiments, the task manager 214 may sequentiallyissue resumption commands to resume agents in order of their priority.Other schemes, however, may also be used.

Referring now to the algorithm of FIG. 20A, a thread of the task manager214 waits for a task as indicated at 218. As indicated at 220, thethread inserts the task in the task queue 216 when received. The threadthen returns to 218.

Referring now to the algorithm of FIG. 20B, another thread of the taskmanager 214 waits for a task to be inserted into the task queue 216 asindicated at 222. As indicated at 224, the thread selects the highestpriority agent task (based on triggering events as well as priority). Asindicated at 226, the thread determines whether there is an agent toinstantiate. If no, the thread transfers the task to the appropriateagent as indicated at 228. The thread then returns to 222. If yes, thethread outputs an agent instantiation command as indicated at 230. Thethread then proceeds to 228. Similar threads may be configured to selectand execute avatar tasks.

Various agents have been discussed herein. In certain embodiments,agents are programs that may interface with the EAS 10 illustrated inFIG. 1. These programs, as described above, may perform certain, and issome cases specialized, functions, algorithms, etc.

Referring now to FIG. 21, an agent 232 may be configured to receive avariety of inputs. The agent 232 may process these inputs, provide avariety of outputs and perform its designated task(s) in accordance withthe inputs. For example, a driver's training agent may train theoccupant 12 of the vehicle 14 both illustrated in FIG. 1, via audio andvisual feedback, to drive the vehicle 14 so as to maximize its useablelifetime. The agent 232 may process vehicle system outputs to determine,for example, if the occupant 12 frequently aggressively brakes and warnthe driver that such behavior may adversely affect any braking systemassociated with the vehicle 14. To facilitate such feedback, the agent232 may include a task generator (not shown) similar to those describedherein to generate the necessary tasks for the avatar to convey thefeedback.

As discussed above, the agent 232 may also output an emotional output,e.g., agent emotion, that, in certain embodiments, is an indicator ofhow well the agent 232 is performing its intended function.

Some agents run as independent programs that use the middleware messagepassing system discussed herein to interact with the EAS 10 illustratedin FIG. 1. They generally have intelligence and have the same status inthe EAS 10 as the occupant 12 illustrated in FIG. 1 or the learningmodule 186 illustrated in FIG. 16.

While embodiments of the invention have been illustrated and described,it is not intended that these embodiments illustrate and describe allpossible forms of the invention. The words used in the specification arewords of description rather than limitation, and it is understood thatvarious changes may be made without departing from the spirit and scopeof the invention.

What is claimed:
 1. A method for emotively conveying information to auser of a device comprising: receiving input indicative of an operatingstate of the device; transforming the input into data representing asimulated emotional state; generating data representing an avatar thatexpresses the simulated emotional state, the simulated emotional stateconveying information to the user about the operating state of thedevice; displaying the avatar; receiving a query from the user regardingthe simulated emotional state expressed by the avatar; and responding tothe query.
 2. The method of claim 1 further comprising determiningwhether a predetermined event has occurred and initiating a spokendialog with the user regarding the operating state of the device if thepredetermined event has occurred.
 3. The method of claim 2 wherein thespoken dialog includes at least one speech characteristic indicative ofthe simulated emotional state expressed by the avatar.
 4. A method forconveying information to a user of a device comprising: a step forreceiving input indicative of an operating state of the device; a stepfor transforming the input into data representing a simulated emotionalstate; a step for generating data representing an avatar that expressesthe simulated emotional state, the simulated emotional state conveyinginformation to the user about the operating state of the device; and astep for displaying the avatar.
 5. A method for emotively conveyinginformation to a user of an automotive vehicle comprising: receivinginput indicative of an operating state of the vehicle; transforming theinput into data representing a simulated emotional state; generatingdata representing a spoken statement that expresses the simulatedemotional state, the simulated emotional state conveying information tothe user about the operating state of the vehicle; playing the spokenstatement; receiving a query from the user regarding the simulatedemotional state; and responding to the query.